Reconstruction of graphs based on random walks

The analysis of complex networks is of major interest in various fields of science. In many applications we face the challenge that the exact topology of a network is unknown but we are instead given information about distances within this network. The theoretical approaches to this problem have so far been focusing on the reconstruction of graphs from shortest path distance matrices. Often, however, movements in networks do not follow shortest paths but occur in a random fashion. In these cases an appropriate distance measure can be defined as the mean length of a random walk between two nodes — a quantity known as the mean first hitting time.In this contribution we investigate whether a graph can be reconstructed from its mean first hitting time matrix and put forward an algorithm for solving this problem. A heuristic method to reduce the computational effort is described and analyzed. In the case of trees we can even give an algorithm for reconstructing graphs from incomplete random walk distance matrices.     

Analysis of the diffusive mass transport in the anode side porous backing layer of a direct methanol fuel cell

In this work we present an analysis of the mass transport in the anode side porous backing layer of a direct methanol fuel cell (DMFC). The effective transport coefficient of different backing layers at various compressions was measured and compared to two different literature models and a single particle random walk simulation which accounts for details of the geometrical fibre microstructure. Based on the measured values of the effective transport coefficient limiting current densities for diffusive transport were calculated taking into account geometric boundary conditions and anisotropic and inhomogeneous backing layer properties. Comparison with the measured values for the limiting current in fuel cell operation shows qualitative agreement. A systematic underestimation indicates that also other transport processes contribute significantly to the mass transfer at the used experimental setup.     

A systematic approach to the problem of odour source localisation

Although chemical sensing is far simpler than vision or hearing, navigation in a chemical diffusion field is still not well understood. Biological studies have already demonstrated the use of various search methods (e.g., chemotaxis and biased random walk), but robotics research could provide new ways to investigate principles of olfactory-based search skills (Webb, 2000; Grasso, 2001). In previous studies on odour source localisation, we have tested three biologically inspired search strategies: chemotaxis, biased random walk, and a combination of these methods (Kadar and Virk, 1998; Lytridis et al., 2001). The main objective of the present paper is to demonstrate how simulation and robot experiments could be used conjointly to systematically study these search strategies. Specifically, simulation studies are used to calibrate and test our three strategies in concentric diffusion fields with various noise levels. An experiment with a mobile robot was also conducted to assess these strategies in a real diffusion field. The results of this experiment are similar to those of simulation studies showing that chemotaxis is a more efficient but less robust strategy than biased random walk. Overall, the combined strategy seems to be superior to chemotaxis and biased random walk in both simulation and robot experiment. Christodoulos Lytridis received a BEng degree in electronic and computer engineering, MSc degree in mobile robotics and PhD in Olfactory based navigation for mobile robots from the University of Portsmouth. His research interests include odor-based cooperative navigation, biologically inspired navigational methods, and odor sensors for mobile robots. He is an associate member of the Institution of Electrical Engineering. Endre E. Kadar is a senior lecturer in psychology at the University of Portsmouth. He began his academic career as a theoretical mathematician at the Institute of Hydraulic Planning, working in applied modelling on a variety of problems in engineering. Then he moved to the University of Connecticut to work as a teaching and research assistant and received his PhD in experimental psychology in 1996. His PhD research project has dealt with the problem of ecological foundation of perceptual control of locomotion. After moving to Portsmouth in 1996, his theoretical work provided the basis for a productive collaboration with engineers in robotics. Also, these novel field theoretical ideas have resulted in radically new approaches to exploratory learning and visual control of high speed driving. Application of these approaches has been supported by several ESRC grants. Gurvinder S. Virk graduated with first class honours in electrical and electronic engineering (University of Manchester) and received a PhD in control theory (Imperial College, London). He has followed an academic career working at Sheffield City Polytechnic, Universities of Southampton, Sheffield and Bradford, Portsmouth and is now professor of control and robotics at the University of Leeds. His main research interests include robotics, building and energy management systems, and application of advanced model-based control systems. He has held several grants from national and European sources, as well as industry (the total value of ￡11M). He has more then 200 publications, including four books. He coordinated the European thematic network CLAWAR and has been involved in developing robots for exploring volcanic environments, inspection of highway bridges and dams, and biomedical applications. He is chairman of the ISO TC184/SC2 Advisory Group on Standards for mobile service robots. He is Fellow of the Institution of Engineering and Technology, Fellow of the Chartered Institution of Building Services Engineers, Fellow of the Institution of Applied Mathematics and Its Applications. He is Charted Engineer and a Charted Mathematician. He has been awarded the Freedom of the City of London for his work in promoting Information Technology.     

定纤生丝的接绪间隔分布解析

利用随机游动模型描述定纤缫丝过程中的接绪间隔，通过其特性函数给出接绪间隔分布可以表示为Г分布的理论证明，解明分布参数与茧丝纤度特性、缫丝工程条件、目的纤度等之间的关系。通过计算机模拟试验对理论结果验证并讨论了其在缫丝工程管理上的应用。     

Neural Networks Approach to the Random Walk Dilemma of Financial Time Series

Predictions of financial time series often show a characteristic one step shift relative to the original data as in a random walk. This has been the cause for opposing views whether such time series do contain information that can be extracted for predictions, or are simply random walks. In this case study, we show that NNs that are capable of extracting weak low frequency periodic signals buried in a strong high frequency signal, consistently predict the next value in the series to be the current value, as in a random walk, when used for one-step-ahead predictions of the detrended S&P 500 time series. In particular for the Time Delay Feed Forward Networks and Elman Networks of various configurations, our study supports the view of the detrended S&P 500 being a random walk series. This is consistent with the long standing hypothesis that some financial time series are random walk series.     

Asymptotically optimal smoothing of averaged LMS estimates for regression parameter tracking

The sequence of estimates formed by the LMS algorithm for a standard linear regression estimation problem is considered. It is known since earlier that smoothing these estimates by simple averaging will lead to, asymptotically, the recursive least-squares algorithm. In this paper, it is first shown that smoothing the LMS estimates using a matrix updating will lead to smoothed estimates with optimal tracking properties, also in case the true parameters are slowly changing as a random walk. The choice of smoothing matrix should be tailored to the properties of the random walk. Second, it is shown that the same accuracy can be obtained also for a modified algorithm, SLAMS, which is based on averages and requires much less computations.     

基于时变信号模型和归一化格型陷波器的科氏流量计信号处理方法

采用具有跟踪频率变化能力的自适应归一化格型陷波器，对频率、幅值和相位均按照随机游动模型变化的科氏流量传感器信号进行滤波，以求得其频率；再采用自适应谱线增强器从含有噪声的数据中提取所需要的信号；然后采用具有重叠窗的滑动Goertzel算法计算两路信号之间的实时相位差，并通过频率和相位差计算时间差，求得质量流量。仿真结果表明该方法是有效的。     

一种从马尔可夫聚类簇发现潜在WEB社区特征的方法

在分析了目前一些典型的社区发现算法的基础上,通过对无主题条件下的隐含社区发现算法的研究,提出将基于流的社区特征和马尔可夫图形聚类算法(MCL)的簇结合起来寻找Web隐含社区的方法.将镜像或近似镜像页面的删除放在图形聚类之后,大大减少了比较的代价.然后,在聚类簇的基础上,使用判定每个簇内元素的筛选算法产生可能的社区候选集合.实验表明,该方法是可行的,可以发现许多存在的社区.     

On memoryless provers and insincere verifiers

In this article, we introduce a Prover–Verifier model for analysing the computational complexity of a class of constraint satisfaction problems (CSPs) termed boolean binary constraint satisfaction problems (BBCSPs). BBCSPs represent an extremely general class of CSPs and find applications in a wide variety of domains including constraint programming, puzzle solving and program testing. The constraints in a BBCSP permit the combination of multiple theories as opposed to traditional constraint systems in which all constraints belong to the same theory. We establish that each instance of a BBCSP admits a coin-flipping Turing machine that halts in time polynomial in the size of the input. Furthermore, the algorithm is oblivious in that it never sees more than one constraint at a time. The prover, P, in the Prover–Verifier model is endowed with very limited powers. In particular, it has no memory and it can only pose restricted queries to the verifier. The verifier, on the other hand, is both omniscient in that it is cognisant of all the problem details and insincere in that it does not have to decide a priori on the intended proof. However, the verifier must stay consistent in its responses, i.e. it cannot rule out a certain possibility in one response to a query from the prover and then rule in the same possibility in response to a subsequent query. We note that the combination of the resources required by the prover and the type of certificate demanded of the verifier, determine the resources required by an algorithm. Inasmuch as our provers will be memoryless and our verifiers will be asked for extremely simple certificates, our work establishes the existence of a simple, randomised algorithm for BBCSPs. Our model itself serves as a basis for the design of zero-knowledge machine learning algorithms in that the prover ends up learning the proof desired by the verifier. Likewise, our work finds applications in the domain of certifying algorithm design, wherein the goal is to provide a proof of correctness of the algorithm on the input instance by providing an easily checkable certificate.     

A random walk simulation of scalar mixing in flows through submerged vegetations

The scalar transport phenomena in vertical two-dimensional flows are studied using the random walk method. The established Lagrangian model is first applied to study the idealized longitudinal dispersion in open channels, before being used to investigate the scalar mixing characteristics of the flows through submerged vegetations. The longitudinal dispersion coefficients of the fully-developed boundary layer flows, with and without vegetations, are calculated based on the positions of the particles. A convenient way of incorporating the effects of vegetations is proposed, where all the flow parameters are regarded to be continually distributed over the depth. The simulation results show high accuracy of the developed random walk method, and indicate that the new method of accounting for the vegetation effects is appropriate for all the test cases considered. The predicted longitudinal dispersion coefficients agree well with the measurements. The merit of the new method is highlighted by its simplicity and efficiency in comparison with the conventional method that assumes the discontinuous distribution of the flow parameters over the depth.